As the world steps into the era of Internet of Things (IoT), numerous miniaturized electronic devices requiring autonomous micropower sources will be connected to the internet. All‐solid‐state thin‐film lithium/lithium‐ion microbatteries (TFBs) combining solid‐state battery architecture and thin‐film manufacturing are regarded as ideal on‐chip power sources for IoT‐enabled microelectronic devices. However, unlike commercialized lithium‐ion batteries, TFBs are still in the immature state, and new advances in materials, manufacturing, and structure are required to improve their performance. In this review, the current status and existing challenges of TFBs for practical application in internet‐connected devices for the IoT are discussed. Recent progress in thin‐film deposition, electrode and electrolyte materials, interface modification, and 3D architecture design is comprehensively summarized and discussed, with emphasis on state‐of‐the‐art strategies to improve the areal capacity and cycling stability of TFBs. Moreover, to be suitable power sources for IoT devices, the design of next‐generation TFBs should consider multiple functionalities, including wide working temperature range, good flexibility, high transparency, and integration with energy‐harvesting systems. Perspectives on designing practically accessible TFBs are provided, which may guide the future development of reliable power sources for IoT devices.
Characteristic
gene selection and tumor classification of gene
expression data play major roles in genomic research. Due to the characteristics
of a small sample size and high dimensionality of gene expression
data, it is a common practice to perform dimensionality reduction
prior to the use of machine learning-based methods to analyze the
expression data. In this context, classical principal component analysis
(PCA) and its improved versions have been widely used. Recently, methods
based on supervised discriminative sparse PCA have been developed
to improve the performance of data dimensionality reduction. However,
such methods still have limitations: most of them have not taken into
consideration the improvement of robustness to outliers and noise,
label information, sparsity, as well as capturing intrinsic geometrical
structures in one objective function. To address this drawback, in
this study, we propose a novel PCA-based method, known as the robust
Laplacian supervised discriminative sparse PCA, termed RLSDSPCA, which
enforces the L2,1 norm on the error function and incorporates the
graph Laplacian into supervised discriminative sparse PCA. To evaluate
the efficacy of the proposed RLSDSPCA, we applied it to the problems
of characteristic gene selection and tumor classification problems
using gene expression data. The results demonstrate that the proposed
RLSDSPCA method, when used in combination with other related methods,
can effectively identify new pathogenic genes associated with diseases.
In addition, RLSDSPCA has also achieved the best performance compared
with the state-of-the-art methods on tumor classification in terms
of major performance metrics. The codes and data sets used in the
study are freely available at .
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